Introduction
- TMLE is a general algorithm for the construction of double robust, semiparametric, efficient substitution estimators. TMLE allows for data-adaptive estimation while obtaining valid statistical inference.
- Although TMLE implemtation uses the G-computation estimand (G-formula). Briefly, the TMLE algorithm uses information in the estimated exposure mechanism P(A|W) to update the initial estimator of the conditional mean E_0(Y|A,W).
- The targeted estimates are then substituted into the parameter mapping. The updating step achieves a targeted bias reduction for the parameter of interest \(\psi\)(P0) (the true target parameter) and serves to solve the efficient score equation. As a result, TMLE is a double robust estimator.
- TMLE it will be consistent for \(\psi\)(P0) is either the conditional expectation E_0(Y|A,W) or the exposure mechanism P_0(A|W) are estimated consistently. When both functions are consistently estimated, the TMLE will be efficient in that it achieves the lowest asymptotic variance among a large class of estimators. These asymptotic properties typically translate into lower bias and variance in finite samples.(Bühlmann et al., 2016)
- The advantages of TMLE have been repeatedly demonstrated in both simulation studies and applied analyses.(Laan and Rose, 2011)
- The procedure is available with standard software such as the tmle package in R (Gruber and Laan, 2011).
TMLE flow chart
Source: (???)
Implementation
First step
Thank you
Thank you for participating in this tutorial.
If you have updates you would like to make to the lesson, please send me a pull request.
Alternatively, if you have any questions, please e-mail me.
Miguel Angel Luque Fernandez
E-mail: miguel-angel.luque at lshtm.ac.uk
Twitter @WATZILEI
Session Info
devtools::session_info()
References
Bühlmann P, Drineas P, Laan M van der, Kane M. (2016). Handbook of big data. CRC Press.
Greenland S, Robins JM. (1986). Identifiability, exchangeability, and epidemiological confounding. International journal of epidemiology 15: 413–419.
Gruber S, Laan M van der. (2011). Tmle: An r package for targeted maximum likelihood estimation. UC Berkeley Division of Biostatistics Working Paper Series.
Laan M van der, Rose S. (2011). Targeted learning: Causal inference for observational and experimental data. Springer Series in Statistics.
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